Overview

Dataset statistics

Number of variables18
Number of observations1176
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory158.6 KiB
Average record size in memory138.1 B

Variable types

Numeric10
Categorical8

Alerts

YearsInCurrentRole is highly overall correlated with YearsSinceLastPromotionHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsInCurrentRoleHigh correlation
EducationField_Life Sciences is highly overall correlated with EducationField_MedicalHigh correlation
EducationField_Medical is highly overall correlated with EducationField_Life SciencesHigh correlation
EducationField_Marketing is highly imbalanced (51.4%)Imbalance
EducationField_Other is highly imbalanced (68.5%)Imbalance
EducationField_Technical Degree is highly imbalanced (56.6%)Imbalance
NumCompaniesWorked has 158 (13.4%) zerosZeros
TrainingTimesLastYear has 41 (3.5%) zerosZeros
YearsInCurrentRole has 193 (16.4%) zerosZeros
YearsSinceLastPromotion has 459 (39.0%) zerosZeros

Reproduction

Analysis started2023-02-06 13:05:42.203450
Analysis finished2023-02-06 13:06:16.467757
Duration34.26 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct43
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.846088
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T08:06:16.782093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median35
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.0386942
Coefficient of variation (CV)0.24530946
Kurtosis-0.34525265
Mean36.846088
Median Absolute Deviation (MAD)6
Skewness0.42932542
Sum43331
Variance81.697993
MonotonicityNot monotonic
2023-02-06T08:06:17.197279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
34 66
 
5.6%
35 62
 
5.3%
32 55
 
4.7%
29 54
 
4.6%
31 53
 
4.5%
38 52
 
4.4%
33 51
 
4.3%
36 50
 
4.3%
40 48
 
4.1%
30 48
 
4.1%
Other values (33) 637
54.2%
ValueCountFrequency (%)
18 6
 
0.5%
19 6
 
0.5%
20 9
 
0.8%
21 11
 
0.9%
22 13
 
1.1%
23 13
 
1.1%
24 21
1.8%
25 19
1.6%
26 30
2.6%
27 36
3.1%
ValueCountFrequency (%)
60 5
 
0.4%
59 9
0.8%
58 10
0.9%
57 4
 
0.3%
56 6
 
0.5%
55 17
1.4%
54 14
1.2%
53 15
1.3%
52 15
1.3%
51 15
1.3%

DailyRate
Real number (ℝ)

Distinct791
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.04592
Minimum103
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T08:06:17.527018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile169.5
Q1466.75
median799.5
Q31154
95-th percentile1418.5
Maximum1499
Range1396
Interquartile range (IQR)687.25

Descriptive statistics

Standard deviation401.1665
Coefficient of variation (CV)0.50017897
Kurtosis-1.2022479
Mean802.04592
Median Absolute Deviation (MAD)343.5
Skewness0.0065894886
Sum943206
Variance160934.56
MonotonicityNot monotonic
2023-02-06T08:06:17.858041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 6
 
0.5%
329 5
 
0.4%
530 5
 
0.4%
267 4
 
0.3%
589 4
 
0.3%
465 4
 
0.3%
408 4
 
0.3%
430 4
 
0.3%
921 4
 
0.3%
1146 4
 
0.3%
Other values (781) 1132
96.3%
ValueCountFrequency (%)
103 1
 
0.1%
104 1
 
0.1%
105 1
 
0.1%
106 1
 
0.1%
107 1
 
0.1%
109 1
 
0.1%
111 3
0.3%
115 1
 
0.1%
116 2
0.2%
117 3
0.3%
ValueCountFrequency (%)
1499 1
 
0.1%
1498 1
 
0.1%
1496 2
0.2%
1495 3
0.3%
1492 1
 
0.1%
1490 3
0.3%
1488 1
 
0.1%
1485 1
 
0.1%
1482 1
 
0.1%
1480 2
0.2%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2593537
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T08:06:18.497708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.188622
Coefficient of variation (CV)0.88436215
Kurtosis-0.27737605
Mean9.2593537
Median Absolute Deviation (MAD)5
Skewness0.94742099
Sum10889
Variance67.053529
MonotonicityNot monotonic
2023-02-06T08:06:18.766087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 170
14.5%
1 163
13.9%
10 70
 
6.0%
3 70
 
6.0%
7 69
 
5.9%
9 61
 
5.2%
8 59
 
5.0%
4 53
 
4.5%
5 51
 
4.3%
6 51
 
4.3%
Other values (19) 359
30.5%
ValueCountFrequency (%)
1 163
13.9%
2 170
14.5%
3 70
6.0%
4 53
 
4.5%
5 51
 
4.3%
6 51
 
4.3%
7 69
5.9%
8 59
 
5.0%
9 61
 
5.2%
10 70
6.0%
ValueCountFrequency (%)
29 22
1.9%
28 20
1.7%
27 10
0.9%
26 21
1.8%
25 22
1.9%
24 21
1.8%
23 21
1.8%
22 15
1.3%
21 15
1.3%
20 23
2.0%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.235544
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T08:06:19.065331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.459483
Coefficient of variation (CV)0.30888978
Kurtosis-1.2119347
Mean66.235544
Median Absolute Deviation (MAD)18
Skewness-0.071664223
Sum77893
Variance418.59043
MonotonicityNot monotonic
2023-02-06T08:06:19.418915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 24
 
2.0%
96 24
 
2.0%
48 24
 
2.0%
87 23
 
2.0%
42 23
 
2.0%
57 23
 
2.0%
84 23
 
2.0%
54 22
 
1.9%
79 21
 
1.8%
73 20
 
1.7%
Other values (61) 949
80.7%
ValueCountFrequency (%)
30 17
1.4%
31 12
1.0%
32 18
1.5%
33 15
1.3%
34 9
0.8%
35 16
1.4%
36 15
1.3%
37 15
1.3%
38 11
0.9%
39 15
1.3%
ValueCountFrequency (%)
100 16
1.4%
99 13
1.1%
98 24
2.0%
97 16
1.4%
96 24
2.0%
95 17
1.4%
94 18
1.5%
93 15
1.3%
92 20
1.7%
91 17
1.4%

MonthlyIncome
Real number (ℝ)

Distinct1090
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6525.2526
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T08:06:19.782221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2098.5
Q12900.25
median4938.5
Q38480.5
95-th percentile17798.25
Maximum19999
Range18990
Interquartile range (IQR)5580.25

Descriptive statistics

Standard deviation4726.3655
Coefficient of variation (CV)0.72431917
Kurtosis0.98919581
Mean6525.2526
Median Absolute Deviation (MAD)2221.5
Skewness1.3685422
Sum7673697
Variance22338531
MonotonicityNot monotonic
2023-02-06T08:06:20.104577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 4
 
0.3%
3452 3
 
0.3%
5562 3
 
0.3%
6347 3
 
0.3%
2451 3
 
0.3%
2886 2
 
0.2%
5238 2
 
0.2%
2564 2
 
0.2%
2028 2
 
0.2%
2693 2
 
0.2%
Other values (1080) 1150
97.8%
ValueCountFrequency (%)
1009 1
0.1%
1052 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
1232 1
0.1%
1359 1
0.1%
1393 1
0.1%
1416 1
0.1%
1420 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19926 1
0.1%
19859 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%
19701 1
0.1%
19665 1
0.1%

MonthlyRate
Real number (ℝ)

Distinct1147
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14372.792
Minimum2097
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T08:06:20.455459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2097
5-th percentile3440.5
Q18222
median14379.5
Q320444.25
95-th percentile25426.5
Maximum26999
Range24902
Interquartile range (IQR)12222.25

Descriptive statistics

Standard deviation7072.7447
Coefficient of variation (CV)0.49209262
Kurtosis-1.2040512
Mean14372.792
Median Absolute Deviation (MAD)6085
Skewness0.018691872
Sum16902403
Variance50023717
MonotonicityNot monotonic
2023-02-06T08:06:20.820446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9150 3
 
0.3%
15318 2
 
0.2%
11591 2
 
0.2%
12355 2
 
0.2%
20364 2
 
0.2%
4156 2
 
0.2%
11162 2
 
0.2%
9096 2
 
0.2%
25326 2
 
0.2%
23016 2
 
0.2%
Other values (1137) 1155
98.2%
ValueCountFrequency (%)
2097 1
0.1%
2104 1
0.1%
2112 1
0.1%
2122 1
0.1%
2125 2
0.2%
2137 1
0.1%
2261 1
0.1%
2323 1
0.1%
2326 1
0.1%
2338 1
0.1%
ValueCountFrequency (%)
26999 1
0.1%
26997 1
0.1%
26968 1
0.1%
26956 1
0.1%
26933 1
0.1%
26914 1
0.1%
26897 1
0.1%
26849 1
0.1%
26841 1
0.1%
26820 1
0.1%

NumCompaniesWorked
Real number (ℝ)

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6879252
Minimum0
Maximum9
Zeros158
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T08:06:21.121880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4744599
Coefficient of variation (CV)0.92058364
Kurtosis0.025929728
Mean2.6879252
Median Absolute Deviation (MAD)1
Skewness1.0237581
Sum3161
Variance6.1229519
MonotonicityNot monotonic
2023-02-06T08:06:21.323679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 406
34.5%
0 158
 
13.4%
3 138
 
11.7%
2 124
 
10.5%
4 105
 
8.9%
6 61
 
5.2%
7 56
 
4.8%
5 49
 
4.2%
8 41
 
3.5%
9 38
 
3.2%
ValueCountFrequency (%)
0 158
 
13.4%
1 406
34.5%
2 124
 
10.5%
3 138
 
11.7%
4 105
 
8.9%
5 49
 
4.2%
6 61
 
5.2%
7 56
 
4.8%
8 41
 
3.5%
9 38
 
3.2%
ValueCountFrequency (%)
9 38
 
3.2%
8 41
 
3.5%
7 56
 
4.8%
6 61
 
5.2%
5 49
 
4.2%
4 105
 
8.9%
3 138
 
11.7%
2 124
 
10.5%
1 406
34.5%
0 158
 
13.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
3
995 
4
181 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

Length

2023-02-06T08:06:21.541313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T08:06:21.764760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

TrainingTimesLastYear
Real number (ℝ)

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7857143
Minimum0
Maximum6
Zeros41
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T08:06:21.935551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2716215
Coefficient of variation (CV)0.45647952
Kurtosis0.61951249
Mean2.7857143
Median Absolute Deviation (MAD)1
Skewness0.60324086
Sum3276
Variance1.6170213
MonotonicityNot monotonic
2023-02-06T08:06:22.116360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 450
38.3%
3 395
33.6%
4 95
 
8.1%
5 89
 
7.6%
1 54
 
4.6%
6 52
 
4.4%
0 41
 
3.5%
ValueCountFrequency (%)
0 41
 
3.5%
1 54
 
4.6%
2 450
38.3%
3 395
33.6%
4 95
 
8.1%
5 89
 
7.6%
6 52
 
4.4%
ValueCountFrequency (%)
6 52
 
4.4%
5 89
 
7.6%
4 95
 
8.1%
3 395
33.6%
2 450
38.3%
1 54
 
4.6%
0 41
 
3.5%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
3
711 
2
274 
4
125 
1
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

Length

2023-02-06T08:06:22.331515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T08:06:22.559343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2355442
Minimum0
Maximum18
Zeros193
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T08:06:22.790273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6474129
Coefficient of variation (CV)0.86114385
Kurtosis0.5713178
Mean4.2355442
Median Absolute Deviation (MAD)3
Skewness0.94752544
Sum4981
Variance13.303621
MonotonicityNot monotonic
2023-02-06T08:06:23.012915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 296
25.2%
0 193
16.4%
7 178
15.1%
3 106
 
9.0%
4 89
 
7.6%
8 71
 
6.0%
9 50
 
4.3%
1 50
 
4.3%
6 28
 
2.4%
5 26
 
2.2%
Other values (9) 89
 
7.6%
ValueCountFrequency (%)
0 193
16.4%
1 50
 
4.3%
2 296
25.2%
3 106
 
9.0%
4 89
 
7.6%
5 26
 
2.2%
6 28
 
2.4%
7 178
15.1%
8 71
 
6.0%
9 50
 
4.3%
ValueCountFrequency (%)
18 2
 
0.2%
17 4
 
0.3%
16 6
 
0.5%
15 6
 
0.5%
14 9
 
0.8%
13 11
 
0.9%
12 8
 
0.7%
11 18
 
1.5%
10 25
2.1%
9 50
4.3%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.172619
Minimum0
Maximum15
Zeros459
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T08:06:23.251690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1773076
Coefficient of variation (CV)1.462432
Kurtosis3.7117997
Mean2.172619
Median Absolute Deviation (MAD)1
Skewness1.9887353
Sum2555
Variance10.095284
MonotonicityNot monotonic
2023-02-06T08:06:23.461720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 459
39.0%
1 291
24.7%
2 130
 
11.1%
7 59
 
5.0%
4 51
 
4.3%
3 38
 
3.2%
5 36
 
3.1%
6 27
 
2.3%
11 18
 
1.5%
8 17
 
1.4%
Other values (6) 50
 
4.3%
ValueCountFrequency (%)
0 459
39.0%
1 291
24.7%
2 130
 
11.1%
3 38
 
3.2%
4 51
 
4.3%
5 36
 
3.1%
6 27
 
2.3%
7 59
 
5.0%
8 17
 
1.4%
9 15
 
1.3%
ValueCountFrequency (%)
15 11
 
0.9%
14 7
 
0.6%
13 5
 
0.4%
12 7
 
0.6%
11 18
 
1.5%
10 5
 
0.4%
9 15
 
1.3%
8 17
 
1.4%
7 59
5.0%
6 27
2.3%

Attrition_1
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
997 
1
179 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%

Length

2023-02-06T08:06:23.698777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T08:06:23.915674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
688 
1
488 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%

Length

2023-02-06T08:06:24.104483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T08:06:24.325951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%

Most occurring characters

ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1052 
1
124 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%

Length

2023-02-06T08:06:24.912704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T08:06:25.154324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
802 
1
374 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%

Length

2023-02-06T08:06:25.353505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T08:06:25.585918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%

Most occurring characters

ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1109 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%

Length

2023-02-06T08:06:25.787210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T08:06:26.012632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1071 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Length

2023-02-06T08:06:26.189654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T08:06:26.406613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Interactions

2023-02-06T08:06:12.514792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:46.397036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:49.218082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:52.012334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:55.195811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:58.039346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:00.725779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:03.817182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:06.616396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:09.184955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:12.797572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:46.730527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:49.481232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:52.252569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:55.460279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:58.278850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:01.015909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:04.050344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:06.857335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:09.495712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:13.121406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:46.982845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:49.880457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:52.532148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:55.725489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:58.530213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:01.320637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:04.299569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:07.111800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:10.219986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:13.413718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:47.213494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:50.150672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:52.796756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:56.002495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:58.787317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:02.013754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:04.568053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:07.359084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:10.499293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:13.706424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:47.481003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:50.416173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:53.097426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:56.297990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:59.058868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:02.275025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:04.861853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:07.619844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:10.792369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:13.995146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:47.754384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:50.694824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:53.893164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:56.590588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:59.329380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:02.546550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:05.169523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:07.876920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:11.077097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:14.240529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:47.987339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:50.948551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:54.141835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:56.859669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:59.581928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:02.794575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:05.452479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:08.114769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:11.324781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:14.517918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:48.317905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:51.198654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:54.393985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:57.134849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:59.818969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:03.030360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:05.736158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:08.347473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:11.577173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:14.789659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:48.647844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:51.468388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:54.644757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:57.428351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:00.110244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:03.304441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:06.008371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:08.599764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:11.854481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:15.071758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:48.947545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:51.739119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:54.907193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:05:57.690543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:00.410049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:03.553962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:06.347550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:08.896686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T08:06:12.213521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-02-06T08:06:26.619344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
AgeDailyRateDistanceFromHomeHourlyRateMonthlyIncomeMonthlyRateNumCompaniesWorkedTrainingTimesLastYearYearsInCurrentRoleYearsSinceLastPromotionPerformanceRatingWorkLifeBalanceAttrition_1EducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical Degree
Age1.0000.017-0.0250.0560.4680.0030.3600.0030.1850.1550.0000.0240.2180.0000.0000.0000.0560.040
DailyRate0.0171.000-0.0020.0280.030-0.0200.049-0.0130.008-0.0190.0000.0370.0660.0000.0560.0000.0000.085
DistanceFromHome-0.025-0.0021.0000.0140.0140.042-0.014-0.0310.014-0.0010.0870.0000.0520.0000.0420.0000.0000.070
HourlyRate0.0560.0280.0141.000-0.002-0.0190.044-0.001-0.033-0.0490.0000.0000.0660.0630.0940.0000.0340.045
MonthlyIncome0.4680.0300.014-0.0021.0000.0440.201-0.0250.3870.2750.0290.0000.1950.0000.1310.0350.0410.031
MonthlyRate0.003-0.0200.042-0.0190.0441.000-0.014-0.014-0.012-0.0090.0000.0000.0000.0450.0000.0000.0510.000
NumCompaniesWorked0.3600.049-0.0140.0440.201-0.0141.000-0.028-0.125-0.0510.0000.0400.0800.1070.0700.0680.0340.000
TrainingTimesLastYear0.003-0.013-0.031-0.001-0.025-0.014-0.0281.000-0.0060.0050.0000.0000.0730.0510.0730.1240.0000.034
YearsInCurrentRole0.1850.0080.014-0.0330.387-0.012-0.125-0.0061.0000.5070.0590.0280.1670.0000.0480.0000.0000.000
YearsSinceLastPromotion0.155-0.019-0.001-0.0490.275-0.009-0.0510.0050.5071.0000.0000.0000.0190.0000.0150.0000.0000.000
PerformanceRating0.0000.0000.0870.0000.0290.0000.0000.0000.0590.0001.0000.0000.0000.0000.0000.0000.0000.000
WorkLifeBalance0.0240.0370.0000.0000.0000.0000.0400.0000.0280.0000.0001.0000.1190.0600.0000.0280.0230.031
Attrition_10.2180.0660.0520.0660.1950.0000.0800.0730.1670.0190.0000.1191.0000.0000.0420.0240.0000.055
EducationField_Life Sciences0.0000.0000.0000.0630.0000.0450.1070.0510.0000.0000.0000.0600.0001.0000.2850.5730.2010.259
EducationField_Marketing0.0000.0560.0420.0940.1310.0000.0700.0730.0480.0150.0000.0000.0420.2851.0000.2300.0730.098
EducationField_Medical0.0000.0000.0000.0000.0350.0000.0680.1240.0000.0000.0000.0280.0240.5730.2301.0000.1610.209
EducationField_Other0.0560.0000.0000.0340.0410.0510.0340.0000.0000.0000.0000.0230.0000.2010.0730.1611.0000.064
EducationField_Technical Degree0.0400.0850.0700.0450.0310.0000.0000.0340.0000.0000.0000.0310.0550.2590.0980.2090.0641.000

Missing values

2023-02-06T08:06:15.550646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-06T08:06:16.211450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeDailyRateDistanceFromHomeHourlyRateMonthlyIncomeMonthlyRateNumCompaniesWorkedPerformanceRatingTrainingTimesLastYearWorkLifeBalanceYearsInCurrentRoleYearsSinceLastPromotionAttrition_1EducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical Degree
63825583487425618154131420001000
135641337854439326841543341001000
49434204143125792912133320000001
1056281496192290915747333421100001
8054510509655593179701323104010000
50032646992632218089132240010000
1176493012272164133498332321000010
61426887588236620898132371100100
125130979159471403088232371001000
1426322672949283715919133324010000
AgeDailyRateDistanceFromHomeHourlyRateMonthlyIncomeMonthlyRateNumCompaniesWorkedPerformanceRatingTrainingTimesLastYearWorkLifeBalanceYearsInCurrentRoleYearsSinceLastPromotionAttrition_1EducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical Degree
10553482915711700711929733286000100
545305012799530425275742277001000
18134629279523115711233321000100
1464261167530296621378032320000010
13774910642421916113738333344010000
353371319651597417001432376000100
116648365489152025602243322000100
108251280764288926897132322000100
98334404298668761631324114000001
83041167124647669051334300010000